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  How feedback inhibition shapes spike-timing-dependent plasticity and its implications for recent Schizophrenia models

Porra, B., McCabea, L., di Prodia, P., Kolodziejski, C., & Wörgötter, F. (2011). How feedback inhibition shapes spike-timing-dependent plasticity and its implications for recent Schizophrenia models. Neurocomputational Models of Brain Disorders, 560-567. doi:10.1016/j.neunet.2011.03.004.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0029-117F-7 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0029-1180-1
Genre: Journal Article

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 Creators:
Porra, Bernd, Author
McCabea, Lynsey, Author
di Prodia, Paolo, Author
Kolodziejski, Christoph1, Author           
Wörgötter, Florentin, Author
Affiliations:
1Max Planck Research Group Network Dynamics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063295              

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Free keywords: Schizophrenia; GABA; NMDA;GAD67; STDP; Hebb; Inhibition; PCP
 Abstract: It has been shown that plasticity is not a fixed property but, in fact, changes depending on the location of the synapse on the neuron and/or changes of biophysical parameters. Here, we investigate how plasticity is shaped by feedback inhibition in a cortical microcircuit. We use a differential Hebbian learning rule to model spike-timing-dependent plasticity and show analytically that the feedback inhibition shortens the time window for LTD during spike-timing-dependent plasticity but not for LTP. We then use a realistic GENESIS model to test two hypothesis about interneuron hypofunction and conclude that a reduction in GAD67 is the most likely candidate as the cause for hypofrontality as observed in Schizophrenia.

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Language(s): eng - English
 Dates: 2011-08
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: eDoc: 630123
DOI: 10.1016/j.neunet.2011.03.004
 Degree: -

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Title: Neurocomputational Models of Brain Disorders
Source Genre: Issue
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Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 560 - 567 Identifier: -

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Title: Neural Networks
Source Genre: Journal
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Pages: - Volume / Issue: 24 (6) Sequence Number: - Start / End Page: - Identifier: -